AirBnb Pricing Prediction¶

Data Manipulation¶

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
from decimal import Decimal
import re
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
from sklearn.neighbors import KNeighborsRegressor
from sklearn.linear_model import Ridge
from sklearn.linear_model import Lasso
from sklearn.linear_model import LinearRegression
from  sklearn.preprocessing  import PolynomialFeatures
from sklearn import svm
from sklearn.model_selection import GridSearchCV,cross_val_score
from sklearn.metrics import r2_score
import warnings
warnings.filterwarnings("ignore")

import seaborn as sns
sns.set()

pd.set_option('display.max_rows', 500)
pd.set_option('display.max_columns', 500)
pd.set_option('display.width', 1000)

listings.csv¶

listings = pd.read_csv('listings.csv.gz')

listings.head(5)

listings.duplicated().sum()

0

listings.shape

(7833, 106)

listings.dtypes

id                                                int64
listing_url                                      object
scrape_id                                         int64
last_scraped                                     object
name                                             object
summary                                          object
space                                            object
description                                      object
experiences_offered                              object
neighborhood_overview                            object
notes                                            object
transit                                          object
access                                           object
interaction                                      object
house_rules                                      object
thumbnail_url                                   float64
medium_url                                      float64
picture_url                                      object
xl_picture_url                                  float64
host_id                                           int64
host_url                                         object
host_name                                        object
host_since                                       object
host_location                                    object
host_about                                       object
host_response_time                               object
host_response_rate                               object
host_acceptance_rate                            float64
host_is_superhost                                object
host_thumbnail_url                               object
host_picture_url                                 object
host_neighbourhood                               object
host_listings_count                             float64
host_total_listings_count                       float64
host_verifications                               object
host_has_profile_pic                             object
host_identity_verified                           object
street                                           object
neighbourhood                                    object
neighbourhood_cleansed                           object
neighbourhood_group_cleansed                    float64
city                                             object
state                                            object
zipcode                                          object
market                                           object
smart_location                                   object
country_code                                     object
country                                          object
latitude                                        float64
longitude                                       float64
is_location_exact                                object
property_type                                    object
room_type                                        object
accommodates                                      int64
bathrooms                                       float64
bedrooms                                        float64
beds                                            float64
bed_type                                         object
amenities                                        object
square_feet                                     float64
price                                            object
weekly_price                                     object
monthly_price                                    object
security_deposit                                 object
cleaning_fee                                     object
guests_included                                   int64
extra_people                                     object
minimum_nights                                    int64
maximum_nights                                    int64
minimum_minimum_nights                            int64
maximum_minimum_nights                            int64
minimum_maximum_nights                            int64
maximum_maximum_nights                            int64
minimum_nights_avg_ntm                          float64
maximum_nights_avg_ntm                          float64
calendar_updated                                 object
has_availability                                 object
availability_30                                   int64
availability_60                                   int64
availability_90                                   int64
availability_365                                  int64
calendar_last_scraped                            object
number_of_reviews                                 int64
number_of_reviews_ltm                             int64
first_review                                     object
last_review                                      object
review_scores_rating                            float64
review_scores_accuracy                          float64
review_scores_cleanliness                       float64
review_scores_checkin                           float64
review_scores_communication                     float64
review_scores_location                          float64
review_scores_value                             float64
requires_license                                 object
license                                          object
jurisdiction_names                               object
instant_bookable                                 object
is_business_travel_ready                         object
cancellation_policy                              object
require_guest_profile_picture                    object
require_guest_phone_verification                 object
calculated_host_listings_count                    int64
calculated_host_listings_count_entire_homes       int64
calculated_host_listings_count_private_rooms      int64
calculated_host_listings_count_shared_rooms       int64
reviews_per_month                               float64
dtype: object

# delete some information: descriptions, empty ones, same value
listings.drop(['listing_url', 'scrape_id', 'last_scraped', 'name', 'summary', 'space', 'description', 
              'experiences_offered', 'neighborhood_overview', 'notes', 'transit', 'access', 'interaction',
              'house_rules', 'thumbnail_url', 'medium_url', 'picture_url', 'xl_picture_url', 'host_url',
              'host_since', 
              'host_location', 'host_about', 'host_acceptance_rate', 'host_thumbnail_url',
              'host_picture_url', 'host_listings_count', 'host_total_listings_count', 
              'host_verifications', 'host_has_profile_pic', 'street', 'neighbourhood_group_cleansed',
              'city', 'state', 'zipcode', 'market', 'smart_location', 'country_code', 'country',
              'bed_type', 'amenities', 'square_feet', 'calendar_updated', 'has_availability', 'availability_30',
              'availability_60', 'availability_90', 'availability_365', 'calendar_last_scraped', 'requires_license',
              'license', 'jurisdiction_names', 'instant_bookable', 'is_business_travel_ready', 
              'require_guest_profile_picture', 'require_guest_phone_verification',], axis=1, inplace=True)

# id: the listing_id used in the reviews.csv
# host_name: not necessary if host_id is used
# weekly_price, monthly_price: too much missing data
# redundant information: minimum_minimum_nights, maximum_minimum_nights, minimum_maximum_nights, maximum_maximum_nights,                             0
#                        
# first_review, last_review: dates, not useful
# calculated_host_*: statistics of listings for each host, 
listings.drop(['host_name', 'weekly_price', 'monthly_price', 
              'minimum_minimum_nights', 'maximum_minimum_nights',
              'minimum_maximum_nights', 'maximum_maximum_nights', 
              'minimum_nights_avg_ntm', 'maximum_nights_avg_ntm',
              'number_of_reviews_ltm', 
              'first_review', 'last_review', #'calculated_host_listings_count',
              #'calculated_host_listings_count_entire_homes', 'calculated_host_listings_count_private_rooms',
              #'calculated_host_listings_count_shared_rooms',
              ], axis=1, inplace=True)

listings['host_neighbourhood'] == listings['neighbourhood']

0       False
1        True
2        True
3        True
4        True
5       False
6        True
7        True
8        True
9        True
10       True
11       True
12      False
13      False
14      False
15      False
16       True
17       True
18      False
19      False
20       True
21      False
22       True
23       True
24       True
25       True
26       True
27       True
28      False
29       True
30      False
31      False
32       True
33       True
34       True
35       True
36       True
37       True
38       True
39       True
40       True
41       True
42       True
43       True
44       True
45       True
46       True
47       True
48       True
49      False
50      False
51       True
52      False
53      False
54       True
55       True
56       True
57       True
58       True
59       True
60       True
61      False
62      False
63       True
64       True
65       True
66       True
67       True
68       True
69       True
70      False
71       True
72       True
73       True
74      False
75       True
76       True
77       True
78       True
79       True
80      False
81      False
82       True
83       True
84       True
85       True
86       True
87      False
88       True
89       True
90       True
91       True
92      False
93       True
94       True
95      False
96       True
97       True
98       True
99      False
100     False
101      True
102     False
103      True
104      True
105      True
106      True
107      True
108      True
109      True
110      True
111      True
112      True
113      True
114     False
115      True
116      True
117      True
118      True
119      True
120      True
121      True
122      True
123      True
124      True
125      True
126      True
127      True
128      True
129     False
130      True
131      True
132      True
133      True
134      True
135      True
136      True
137      True
138      True
139      True
140      True
141      True
142      True
143      True
144      True
145      True
146      True
147      True
148     False
149     False
150      True
151      True
152      True
153      True
154      True
155      True
156      True
157      True
158     False
159     False
160      True
161      True
162      True
163      True
164      True
165      True
166      True
167      True
168      True
169      True
170      True
171      True
172      True
173      True
174     False
175      True
176      True
177      True
178      True
179      True
180      True
181      True
182      True
183      True
184     False
185      True
186      True
187      True
188      True
189      True
190      True
191     False
192      True
193      True
194      True
195      True
196      True
197      True
198     False
199      True
200      True
201      True
202      True
203      True
204      True
205      True
206      True
207     False
208     False
209     False
210      True
211      True
212      True
213      True
214     False
215      True
216      True
217      True
218      True
219      True
220      True
221      True
222      True
223      True
224      True
225      True
226      True
227      True
228      True
229      True
230      True
231      True
232      True
233      True
234      True
235      True
236     False
237     False
238      True
239     False
240      True
241      True
242      True
243      True
244      True
245     False
246      True
247      True
248      True
249      True
        ...  
7583     True
7584     True
7585    False
7586     True
7587     True
7588    False
7589     True
7590     True
7591    False
7592     True
7593     True
7594     True
7595    False
7596     True
7597     True
7598    False
7599     True
7600     True
7601    False
7602    False
7603    False
7604     True
7605    False
7606     True
7607     True
7608     True
7609     True
7610    False
7611     True
7612     True
7613    False
7614     True
7615     True
7616     True
7617     True
7618     True
7619     True
7620     True
7621    False
7622    False
7623     True
7624     True
7625    False
7626     True
7627    False
7628    False
7629    False
7630    False
7631    False
7632    False
7633     True
7634    False
7635     True
7636     True
7637     True
7638     True
7639    False
7640    False
7641    False
7642    False
7643    False
7644     True
7645     True
7646     True
7647     True
7648     True
7649     True
7650     True
7651    False
7652     True
7653    False
7654     True
7655     True
7656     True
7657    False
7658     True
7659     True
7660    False
7661    False
7662    False
7663     True
7664    False
7665    False
7666     True
7667    False
7668     True
7669    False
7670    False
7671    False
7672     True
7673     True
7674     True
7675    False
7676     True
7677    False
7678    False
7679    False
7680    False
7681    False
7682     True
7683     True
7684    False
7685    False
7686     True
7687     True
7688     True
7689    False
7690    False
7691    False
7692     True
7693     True
7694     True
7695     True
7696     True
7697     True
7698     True
7699    False
7700    False
7701    False
7702    False
7703     True
7704    False
7705    False
7706     True
7707     True
7708    False
7709    False
7710    False
7711    False
7712    False
7713    False
7714    False
7715    False
7716    False
7717    False
7718    False
7719    False
7720     True
7721     True
7722    False
7723    False
7724    False
7725    False
7726     True
7727     True
7728     True
7729     True
7730     True
7731     True
7732    False
7733     True
7734    False
7735    False
7736    False
7737    False
7738    False
7739    False
7740     True
7741     True
7742     True
7743     True
7744     True
7745     True
7746    False
7747     True
7748     True
7749     True
7750    False
7751    False
7752     True
7753    False
7754     True
7755    False
7756     True
7757    False
7758    False
7759    False
7760     True
7761     True
7762    False
7763     True
7764     True
7765    False
7766    False
7767     True
7768     True
7769    False
7770     True
7771     True
7772     True
7773    False
7774    False
7775     True
7776    False
7777    False
7778     True
7779     True
7780     True
7781     True
7782    False
7783     True
7784     True
7785    False
7786     True
7787    False
7788    False
7789     True
7790    False
7791    False
7792    False
7793    False
7794    False
7795    False
7796     True
7797     True
7798     True
7799     True
7800     True
7801     True
7802     True
7803    False
7804    False
7805     True
7806     True
7807     True
7808     True
7809    False
7810    False
7811     True
7812     True
7813     True
7814     True
7815     True
7816    False
7817    False
7818     True
7819     True
7820     True
7821     True
7822    False
7823    False
7824     True
7825    False
7826    False
7827     True
7828    False
7829     True
7830    False
7831     True
7832    False
Length: 7833, dtype: bool

listings.iloc[11] # the one with False from above commend
                  # based on lat and long, Hayes Valley is the correct place not Lower Haight,
                  # and there is overlap between "Hayes Valley" and "Lower Haight"
                  # "Western Addition" is quite larger, contains several microhoods
                  # combined with other datat "neightborhood",
                  # "neighbourhood_cleansed" is suggested

id                                                                    10820
host_id                                                               38836
host_response_time                                       within a few hours
host_response_rate                                                     100%
host_is_superhost                                                         f
host_neighbourhood                                             Lower Haight
host_identity_verified                                                    t
neighbourhood                                                  Lower Haight
neighbourhood_cleansed                                       Haight Ashbury
latitude                                                            37.7719
longitude                                                          -122.439
is_location_exact                                                         t
property_type                                                     Apartment
room_type                                                   Entire home/apt
accommodates                                                              3
bathrooms                                                                 1
bedrooms                                                                  3
beds                                                                      3
price                                                               $177.00
security_deposit                                                  $2,500.00
cleaning_fee                                                        $208.00
guests_included                                                           6
extra_people                                                          $0.00
minimum_nights                                                           30
maximum_nights                                                         1125
number_of_reviews                                                        37
review_scores_rating                                                     89
review_scores_accuracy                                                    9
review_scores_cleanliness                                                 9
review_scores_checkin                                                    10
review_scores_communication                                               9
review_scores_location                                                    9
review_scores_value                                                       9
cancellation_policy                             strict_14_with_grace_period
calculated_host_listings_count                                           40
calculated_host_listings_count_entire_homes                              40
calculated_host_listings_count_private_rooms                              0
calculated_host_listings_count_shared_rooms                               0
reviews_per_month                                                      0.31
Name: 11, dtype: object

listings.drop(['host_neighbourhood', 'neighbourhood'], axis=1, inplace=True)

listings.drop(['id', 'host_id', 'latitude', 'longitude'], axis=1, inplace=True)

listings.shape

(7833, 33)

# Superhosts are experienced hosts who provide a shining example for other hosts,
# and extraordinary experiences for their guests.

# review_scores_rating is calculated from other six ratings.
# Overall Experience. What was your guest’s overall experience?
# Cleanliness. Did your guests feel that your space was clean and tidy?
# Accuracy. How accurately did your listing page represent your space?
# Value. Did your guest feel your listing provided good value for the price?
# Communication. How well did you communicate with your guest before and during their stay?
# Arrival. How smoothly did their check-in go?
# Location. How did guests feel about your neighborhood?

listings.isnull().sum()

host_response_time                               791
host_response_rate                               791
host_is_superhost                                  1
host_identity_verified                             1
neighbourhood_cleansed                             0
is_location_exact                                  0
property_type                                      0
room_type                                          0
accommodates                                       0
bathrooms                                         18
bedrooms                                           3
beds                                              11
price                                              0
security_deposit                                1597
cleaning_fee                                     848
guests_included                                    0
extra_people                                       0
minimum_nights                                     0
maximum_nights                                     0
number_of_reviews                                  0
review_scores_rating                            1607
review_scores_accuracy                          1611
review_scores_cleanliness                       1610
review_scores_checkin                           1613
review_scores_communication                     1609
review_scores_location                          1613
review_scores_value                             1613
cancellation_policy                                1
calculated_host_listings_count                     0
calculated_host_listings_count_entire_homes        0
calculated_host_listings_count_private_rooms       0
calculated_host_listings_count_shared_rooms        0
reviews_per_month                               1558
dtype: int64

listings.fillna({'review_scores_rating':listings['review_scores_rating'].mean()}, inplace=True) 
listings.fillna({'review_scores_accuracy':listings['review_scores_accuracy'].mean()}, inplace=True) 
listings.fillna({'review_scores_cleanliness':listings['review_scores_cleanliness'].mean()}, inplace=True) 
listings.fillna({'review_scores_checkin':listings['review_scores_checkin'].mean()}, inplace=True) 
listings.fillna({'review_scores_communication':listings['review_scores_communication'].mean()}, inplace=True) 
listings.fillna({'review_scores_location':listings['review_scores_location'].mean()}, inplace=True) 
listings.fillna({'review_scores_value':listings['review_scores_value'].mean()}, inplace=True)

listings[listings['number_of_reviews']==0]['reviews_per_month']

137    NaN
258    NaN
410    NaN
497    NaN
601    NaN
646    NaN
755    NaN
792    NaN
809    NaN
810    NaN
894    NaN
941    NaN
1038   NaN
1060   NaN
1087   NaN
1102   NaN
1140   NaN
1190   NaN
1286   NaN
1317   NaN
1367   NaN
1408   NaN
1431   NaN
1442   NaN
1455   NaN
1467   NaN
1484   NaN
1503   NaN
1521   NaN
1535   NaN
1562   NaN
1602   NaN
1622   NaN
1678   NaN
1688   NaN
1715   NaN
1719   NaN
1749   NaN
1873   NaN
1946   NaN
1958   NaN
1962   NaN
1991   NaN
2012   NaN
2032   NaN
2049   NaN
2051   NaN
2073   NaN
2074   NaN
2075   NaN
2079   NaN
2080   NaN
2081   NaN
2092   NaN
2111   NaN
2136   NaN
2181   NaN
2193   NaN
2355   NaN
2373   NaN
2383   NaN
2401   NaN
2402   NaN
2403   NaN
2421   NaN
2422   NaN
2440   NaN
2453   NaN
2463   NaN
2468   NaN
2477   NaN
2535   NaN
2566   NaN
2575   NaN
2586   NaN
2592   NaN
2593   NaN
2648   NaN
2655   NaN
2671   NaN
2731   NaN
2739   NaN
2747   NaN
2758   NaN
2778   NaN
2784   NaN
2794   NaN
2822   NaN
2853   NaN
2885   NaN
2899   NaN
2910   NaN
2925   NaN
2944   NaN
2957   NaN
2965   NaN
2968   NaN
2980   NaN
2991   NaN
2994   NaN
2996   NaN
2997   NaN
3001   NaN
3013   NaN
3041   NaN
3053   NaN
3126   NaN
3222   NaN
3236   NaN
3317   NaN
3363   NaN
3364   NaN
3378   NaN
3408   NaN
3450   NaN
3467   NaN
3469   NaN
3497   NaN
3499   NaN
3511   NaN
3519   NaN
3523   NaN
3525   NaN
3532   NaN
3559   NaN
3564   NaN
3574   NaN
3580   NaN
3600   NaN
3620   NaN
3629   NaN
3637   NaN
3638   NaN
3639   NaN
3647   NaN
3657   NaN
3661   NaN
3719   NaN
3720   NaN
3725   NaN
3729   NaN
3734   NaN
3742   NaN
3750   NaN
3795   NaN
3796   NaN
3802   NaN
3814   NaN
3823   NaN
3922   NaN
3926   NaN
3935   NaN
3938   NaN
3939   NaN
3947   NaN
3948   NaN
3953   NaN
3971   NaN
3972   NaN
3990   NaN
4007   NaN
4008   NaN
4022   NaN
4023   NaN
4070   NaN
4075   NaN
4083   NaN
4104   NaN
4128   NaN
4135   NaN
4136   NaN
4140   NaN
4172   NaN
4174   NaN
4183   NaN
4188   NaN
4189   NaN
4190   NaN
4191   NaN
4198   NaN
4218   NaN
4220   NaN
4225   NaN
4226   NaN
4251   NaN
4256   NaN
4258   NaN
4276   NaN
4285   NaN
4288   NaN
4295   NaN
4307   NaN
4308   NaN
4309   NaN
4311   NaN
4314   NaN
4316   NaN
4318   NaN
4322   NaN
4366   NaN
4368   NaN
4381   NaN
4382   NaN
4383   NaN
4385   NaN
4387   NaN
4394   NaN
4402   NaN
4403   NaN
4413   NaN
4416   NaN
4417   NaN
4427   NaN
4432   NaN
4444   NaN
4455   NaN
4464   NaN
4468   NaN
4470   NaN
4472   NaN
4477   NaN
4483   NaN
4487   NaN
4492   NaN
4499   NaN
4518   NaN
4533   NaN
4534   NaN
4551   NaN
4556   NaN
4561   NaN
4576   NaN
4585   NaN
4587   NaN
4588   NaN
4590   NaN
4591   NaN
4620   NaN
4623   NaN
4630   NaN
4631   NaN
4646   NaN
4647   NaN
4648   NaN
4658   NaN
4660   NaN
4671   NaN
4684   NaN
4687   NaN
4691   NaN
        ..
7518   NaN
7519   NaN
7521   NaN
7522   NaN
7523   NaN
7524   NaN
7525   NaN
7526   NaN
7527   NaN
7528   NaN
7529   NaN
7533   NaN
7534   NaN
7535   NaN
7536   NaN
7538   NaN
7539   NaN
7540   NaN
7542   NaN
7546   NaN
7547   NaN
7549   NaN
7550   NaN
7551   NaN
7553   NaN
7555   NaN
7556   NaN
7557   NaN
7558   NaN
7559   NaN
7561   NaN
7562   NaN
7563   NaN
7569   NaN
7570   NaN
7571   NaN
7572   NaN
7573   NaN
7575   NaN
7576   NaN
7577   NaN
7578   NaN
7583   NaN
7585   NaN
7586   NaN
7587   NaN
7588   NaN
7590   NaN
7593   NaN
7594   NaN
7595   NaN
7596   NaN
7597   NaN
7598   NaN
7599   NaN
7601   NaN
7602   NaN
7603   NaN
7607   NaN
7610   NaN
7611   NaN
7612   NaN
7613   NaN
7614   NaN
7615   NaN
7616   NaN
7617   NaN
7618   NaN
7619   NaN
7620   NaN
7621   NaN
7622   NaN
7623   NaN
7625   NaN
7627   NaN
7628   NaN
7629   NaN
7630   NaN
7631   NaN
7632   NaN
7633   NaN
7634   NaN
7635   NaN
7639   NaN
7640   NaN
7641   NaN
7642   NaN
7644   NaN
7645   NaN
7646   NaN
7647   NaN
7648   NaN
7649   NaN
7650   NaN
7651   NaN
7654   NaN
7655   NaN
7656   NaN
7657   NaN
7659   NaN
7660   NaN
7661   NaN
7662   NaN
7663   NaN
7664   NaN
7665   NaN
7666   NaN
7667   NaN
7668   NaN
7669   NaN
7670   NaN
7671   NaN
7673   NaN
7674   NaN
7675   NaN
7678   NaN
7680   NaN
7681   NaN
7682   NaN
7683   NaN
7684   NaN
7685   NaN
7686   NaN
7687   NaN
7688   NaN
7689   NaN
7693   NaN
7696   NaN
7697   NaN
7699   NaN
7700   NaN
7701   NaN
7702   NaN
7703   NaN
7704   NaN
7705   NaN
7706   NaN
7707   NaN
7708   NaN
7709   NaN
7710   NaN
7711   NaN
7712   NaN
7713   NaN
7714   NaN
7715   NaN
7716   NaN
7717   NaN
7718   NaN
7719   NaN
7721   NaN
7722   NaN
7723   NaN
7724   NaN
7725   NaN
7726   NaN
7727   NaN
7732   NaN
7733   NaN
7735   NaN
7736   NaN
7737   NaN
7738   NaN
7740   NaN
7741   NaN
7742   NaN
7743   NaN
7744   NaN
7746   NaN
7748   NaN
7749   NaN
7750   NaN
7751   NaN
7752   NaN
7753   NaN
7754   NaN
7755   NaN
7756   NaN
7757   NaN
7758   NaN
7759   NaN
7761   NaN
7762   NaN
7763   NaN
7765   NaN
7766   NaN
7767   NaN
7768   NaN
7769   NaN
7770   NaN
7771   NaN
7773   NaN
7774   NaN
7775   NaN
7776   NaN
7777   NaN
7778   NaN
7779   NaN
7780   NaN
7781   NaN
7782   NaN
7783   NaN
7784   NaN
7785   NaN
7786   NaN
7787   NaN
7788   NaN
7789   NaN
7790   NaN
7791   NaN
7792   NaN
7793   NaN
7794   NaN
7795   NaN
7796   NaN
7797   NaN
7798   NaN
7799   NaN
7800   NaN
7801   NaN
7802   NaN
7803   NaN
7804   NaN
7805   NaN
7806   NaN
7807   NaN
7809   NaN
7810   NaN
7811   NaN
7812   NaN
7813   NaN
7814   NaN
7815   NaN
7816   NaN
7817   NaN
7818   NaN
7819   NaN
7820   NaN
7821   NaN
7822   NaN
7823   NaN
7824   NaN
7825   NaN
7826   NaN
7827   NaN
7828   NaN
7829   NaN
7830   NaN
7831   NaN
7832   NaN
Name: reviews_per_month, Length: 1558, dtype: float64

listings.fillna({'reviews_per_month':0}, inplace=True)

# to remove the "$" charactor, we need to change the data type, should not run the second time
listings['price'] = [float(Decimal(re.sub(r'[^\d.]', '', x))) if type(x) == str else x for x in listings['price']]
listings['extra_people'] = [float(Decimal(re.sub(r'[^\d.]', '', x))) if type(x) == str else x for x in listings['extra_people']]
listings['security_deposit'] = [float(Decimal(re.sub(r'[^\d.]', '', x))) if type(x) == str else x for x in listings['security_deposit']]
listings['cleaning_fee'] = [float(Decimal(re.sub(r'[^\d.]', '', x))) if type(x) == str else x for x in listings['cleaning_fee']]

# default should be zero
listings.fillna({'security_deposit':0}, inplace=True) 
listings.fillna({'cleaning_fee':0}, inplace=True)

listings['host_response_rate'] = [float(x.strip('%')) if type(x) == str else x for x in listings['host_response_rate']]

listings = listings.dropna(how='any')

listings.shape

(7012, 33)

listings.isnull().sum()

host_response_time                              0
host_response_rate                              0
host_is_superhost                               0
host_identity_verified                          0
neighbourhood_cleansed                          0
is_location_exact                               0
property_type                                   0
room_type                                       0
accommodates                                    0
bathrooms                                       0
bedrooms                                        0
beds                                            0
price                                           0
security_deposit                                0
cleaning_fee                                    0
guests_included                                 0
extra_people                                    0
minimum_nights                                  0
maximum_nights                                  0
number_of_reviews                               0
review_scores_rating                            0
review_scores_accuracy                          0
review_scores_cleanliness                       0
review_scores_checkin                           0
review_scores_communication                     0
review_scores_location                          0
review_scores_value                             0
cancellation_policy                             0
calculated_host_listings_count                  0
calculated_host_listings_count_entire_homes     0
calculated_host_listings_count_private_rooms    0
calculated_host_listings_count_shared_rooms     0
reviews_per_month                               0
dtype: int64

################# drop some row with strange values ###########################

print(listings.describe().loc[['min','max']])

     host_response_rate  accommodates  bathrooms  bedrooms  beds   price  security_deposit  cleaning_fee  guests_included  extra_people  minimum_nights  maximum_nights  number_of_reviews  review_scores_rating  review_scores_accuracy  review_scores_cleanliness  review_scores_checkin  review_scores_communication  review_scores_location  review_scores_value  calculated_host_listings_count  calculated_host_listings_count_entire_homes  calculated_host_listings_count_private_rooms  calculated_host_listings_count_shared_rooms  reviews_per_month
min                 0.0           1.0        0.0       0.0   0.0     0.0               0.0           0.0              1.0           0.0             1.0             1.0                0.0                  20.0                     2.0                        2.0                    2.0                          2.0                     2.0                  2.0                             1.0                                          0.0                                           0.0                                          0.0               0.00
max               100.0          16.0       10.0       8.0  20.0  8000.0            5100.0         700.0             16.0         300.0           365.0        100000.0              730.0                 100.0                    10.0                       10.0                   10.0                         10.0                    10.0                 10.0                           254.0                                        254.0                                          43.0                                         25.0              18.61

sns.relplot(x="bedrooms", y="room_type", col="host_is_superhost",
            data=listings);

listings_0_bedroom = listings[listings['bedrooms']==0]

listings_0_bedroom.shape

(754, 33)

listings_14_bedroom = listings[listings['bedrooms']==14]

listings_14_bedroom.shape

(0, 33)

listings = listings[listings['bedrooms']>0]
listings = listings[listings['bedrooms']<14]

sns.distplot(listings['beds'])

<matplotlib.axes._subplots.AxesSubplot at 0x2808abdd2e8>

listings_0_bed = listings[listings['beds']==0]

listings_0_bed.shape

(26, 33)

listings = listings[listings['beds']>0]

sns.distplot(listings['price'])

<matplotlib.axes._subplots.AxesSubplot at 0x2808b6adac8>

print(min(listings['price']), max(listings['price']))

0.0 8000.0

sum(listings['price']>500)

357

listings = listings[listings['price']>0]
listings = listings[listings['price']<500]

# minimum_nights, maximum_nights: some big values exist, they are strange but should not be directly dropped

# final:
listings.shape

(5829, 33)

############################### some simple visulization ###########################################

ax = sns.countplot(x="property_type", data=listings)

ax.set_xticklabels(ax.get_xticklabels(), rotation=40, ha="right")
plt.tight_layout()
plt.show()

# attributes about the room 
listings_room = listings[['room_type', 'accommodates', 'bathrooms', 'bedrooms', 'beds']]
sns.pairplot(data=listings_room, hue="room_type")

<seaborn.axisgrid.PairGrid at 0x2808b7ed940>

# attributes about the reviews
lisitngs_review = listings[['review_scores_rating', 'review_scores_accuracy', 'review_scores_cleanliness', 
                           'review_scores_checkin', 'review_scores_communication', 'review_scores_location', 
                           'review_scores_value']]

sns.pairplot(data=lisitngs_review) 
# note: all review scores are skewed

<seaborn.axisgrid.PairGrid at 0x2808b7ed710>

sns.catplot(x="room_type", y="price", hue="host_is_superhost",
            kind="violin", split=True, data=listings)

<seaborn.axisgrid.FacetGrid at 0x2808fd1deb8>

sns.catplot(x="bedrooms", y="price", hue="host_is_superhost",
            kind="violin", split=True, data=listings)

<seaborn.axisgrid.FacetGrid at 0x2808fc43cc0>

sns.relplot(x="number_of_reviews", y="price", col="room_type",
            kind="line", data=listings);

sns.distplot(listings["price"])
plt.show()
sns.boxplot("price",data = listings)
plt.show()
listings["price"].skew()

0.9959401003670516

corr = listings.corr(method='kendall')
plt.figure(figsize=(15,8))
sns.heatmap(corr, annot=True)
listings.columns

Index(['host_response_time', 'host_response_rate', 'host_is_superhost', 'host_identity_verified', 'neighbourhood_cleansed', 'is_location_exact', 'property_type', 'room_type', 'accommodates', 'bathrooms', 'bedrooms', 'beds', 'price', 'security_deposit', 'cleaning_fee', 'guests_included', 'extra_people', 'minimum_nights', 'maximum_nights', 'number_of_reviews', 'review_scores_rating', 'review_scores_accuracy', 'review_scores_cleanliness', 'review_scores_checkin', 'review_scores_communication', 'review_scores_location', 'review_scores_value', 'cancellation_policy', 'calculated_host_listings_count', 'calculated_host_listings_count_entire_homes', 'calculated_host_listings_count_private_rooms', 'calculated_host_listings_count_shared_rooms', 'reviews_per_month'], dtype='object')

listings['host_response_time'].unique()

array(['within an hour', 'within a day', 'within a few hours',
       'a few days or more'], dtype=object)

response_time = pd.get_dummies(listings["host_response_time"],columns = "host_response_time", prefix = "host_response_time")
response_time

listings = pd.concat([listings, response_time], axis=1)
listings.drop(['host_response_time'],axis=1,inplace= True)
listings

listings["host_is_superhost"].unique()

array(['t', 'f'], dtype=object)

listings.describe()

listings["host_is_superhost"] = listings["host_is_superhost"].map({'f' : 0, 't' : 1}).astype(int)

listings["neighbourhood_cleansed"].nunique()

36

series = pd.value_counts(listings.neighbourhood_cleansed)
mask = (series/series.sum() * 100).lt(1)
listings['neighbourhood_cleansed'] = np.where(listings['neighbourhood_cleansed'].isin(series[mask].index),'Other',listings['neighbourhood_cleansed'])
print(listings['neighbourhood_cleansed'].nunique())
pvt = listings.pivot_table(index= "neighbourhood_cleansed", values="calculated_host_listings_count", aggfunc='count')
pvt = pvt.sort_values("calculated_host_listings_count",ascending=False)
pvt

28

pvt.plot(kind='bar', color='blue')

<matplotlib.axes._subplots.AxesSubplot at 0x280909c4f98>

neighborhood = pd.get_dummies(listings["neighbourhood_cleansed"],columns = "neighbourhood_cleansed", prefix = "neighbourhood")
listings = pd.concat([listings, neighborhood], axis=1)
listings.drop(["neighbourhood_cleansed"],axis=1,inplace= True)
listings

listings["is_location_exact"] = listings["is_location_exact"].map({'f' : 0, 't' : 1}).astype(int)

listings["property_type"].unique()

array(['Apartment', 'House', 'Condominium', 'Townhouse', 'Loft',
       'Guest suite', 'Cottage', 'Guesthouse', 'Serviced apartment',
       'Hostel', 'Boutique hotel', 'Bungalow', 'Bed and breakfast',
       'Hotel', 'Boat', 'Other', 'Camper/RV', 'Tiny house', 'Resort',
       'Villa', 'Aparthotel', 'Cabin', 'Earth house', 'Dome house'],
      dtype=object)

listings["property_type"].nunique()

24

pivot = listings.pivot_table(index= "property_type", values="calculated_host_listings_count", aggfunc='count')
pivot = pivot.sort_values("calculated_host_listings_count",ascending=False)
pivot.plot(kind='bar', color='pink')
plt.show()
pivot

series1 = pd.value_counts(listings.property_type)
mask1 = (series1/series1.sum() * 100).lt(1)
listings['property_type'] = np.where(listings['property_type'].isin(series1[mask1].index),'Other',listings['property_type'])
print(listings['property_type'].nunique())
pivot1 = listings.pivot_table(index= "property_type", values="calculated_host_listings_count", aggfunc='count')
pivot1 = pivot1.sort_values("calculated_host_listings_count",ascending=False)
pivot1.plot(kind='bar', color='pink')
plt.show()
pivot1

10

property_type = pd.get_dummies(listings["property_type"],columns = "property_type", prefix = "property_type")
listings = pd.concat([listings, property_type], axis=1)
listings.drop(['property_type'],axis=1,inplace= True)
listings

listings["room_type"].unique()

array(['Entire home/apt', 'Private room', 'Shared room'], dtype=object)

room_type = pd.get_dummies(listings["room_type"],columns = "room_type", prefix = "room_type")
listings = pd.concat([listings, room_type], axis=1)
listings.drop(['room_type'],axis=1,inplace= True)
listings

listings["cancellation_policy"].unique()

array(['moderate', 'strict_14_with_grace_period', 'flexible',
       'super_strict_30', 'strict', 'super_strict_60'], dtype=object)

pivot4 = listings.pivot_table(index= "cancellation_policy", values="calculated_host_listings_count", aggfunc='count')
pivot4 = pivot4.sort_values("calculated_host_listings_count",ascending=False)
pivot4

listings["cancellation_policy"] = listings["cancellation_policy"].map({'strict_14_with_grace_period' : 'strict_with_grace_period', 'super_strict_30' : 'super_strict', 'moderate': 'moderate','flexible': 'flexible','super_strict_60':'super_strict','strict' : 'strict'})

pivot4 = listings.pivot_table(index= "cancellation_policy", values="calculated_host_listings_count", aggfunc='count')
pivot4 = pivot4.sort_values("calculated_host_listings_count",ascending=False)
pivot4

cancellation_policy = pd.get_dummies(listings["cancellation_policy"],columns = "cancellation_policy", prefix = "cancellation")
listings = pd.concat([listings, cancellation_policy], axis=1)
listings.drop(['cancellation_policy'],axis=1,inplace= True)
listings

listings.info()

<class 'pandas.core.frame.DataFrame'>
Int64Index: 5829 entries, 0 to 7832
Data columns (total 78 columns):
host_response_rate                              5829 non-null float64
host_is_superhost                               5829 non-null int32
host_identity_verified                          5829 non-null object
is_location_exact                               5829 non-null int32
accommodates                                    5829 non-null int64
bathrooms                                       5829 non-null float64
bedrooms                                        5829 non-null float64
beds                                            5829 non-null float64
price                                           5829 non-null float64
security_deposit                                5829 non-null float64
cleaning_fee                                    5829 non-null float64
guests_included                                 5829 non-null int64
extra_people                                    5829 non-null float64
minimum_nights                                  5829 non-null int64
maximum_nights                                  5829 non-null int64
number_of_reviews                               5829 non-null int64
review_scores_rating                            5829 non-null float64
review_scores_accuracy                          5829 non-null float64
review_scores_cleanliness                       5829 non-null float64
review_scores_checkin                           5829 non-null float64
review_scores_communication                     5829 non-null float64
review_scores_location                          5829 non-null float64
review_scores_value                             5829 non-null float64
calculated_host_listings_count                  5829 non-null int64
calculated_host_listings_count_entire_homes     5829 non-null int64
calculated_host_listings_count_private_rooms    5829 non-null int64
calculated_host_listings_count_shared_rooms     5829 non-null int64
reviews_per_month                               5829 non-null float64
host_response_time_a few days or more           5829 non-null uint8
host_response_time_within a day                 5829 non-null uint8
host_response_time_within a few hours           5829 non-null uint8
host_response_time_within an hour               5829 non-null uint8
neighbourhood_Bayview                           5829 non-null uint8
neighbourhood_Bernal Heights                    5829 non-null uint8
neighbourhood_Castro/Upper Market               5829 non-null uint8
neighbourhood_Chinatown                         5829 non-null uint8
neighbourhood_Downtown/Civic Center             5829 non-null uint8
neighbourhood_Excelsior                         5829 non-null uint8
neighbourhood_Financial District                5829 non-null uint8
neighbourhood_Haight Ashbury                    5829 non-null uint8
neighbourhood_Inner Richmond                    5829 non-null uint8
neighbourhood_Inner Sunset                      5829 non-null uint8
neighbourhood_Marina                            5829 non-null uint8
neighbourhood_Mission                           5829 non-null uint8
neighbourhood_Nob Hill                          5829 non-null uint8
neighbourhood_Noe Valley                        5829 non-null uint8
neighbourhood_North Beach                       5829 non-null uint8
neighbourhood_Ocean View                        5829 non-null uint8
neighbourhood_Other                             5829 non-null uint8
neighbourhood_Outer Mission                     5829 non-null uint8
neighbourhood_Outer Richmond                    5829 non-null uint8
neighbourhood_Outer Sunset                      5829 non-null uint8
neighbourhood_Pacific Heights                   5829 non-null uint8
neighbourhood_Parkside                          5829 non-null uint8
neighbourhood_Potrero Hill                      5829 non-null uint8
neighbourhood_Russian Hill                      5829 non-null uint8
neighbourhood_South of Market                   5829 non-null uint8
neighbourhood_Visitacion Valley                 5829 non-null uint8
neighbourhood_West of Twin Peaks                5829 non-null uint8
neighbourhood_Western Addition                  5829 non-null uint8
property_type_Apartment                         5829 non-null uint8
property_type_Boutique hotel                    5829 non-null uint8
property_type_Condominium                       5829 non-null uint8
property_type_Guest suite                       5829 non-null uint8
property_type_Hostel                            5829 non-null uint8
property_type_Hotel                             5829 non-null uint8
property_type_House                             5829 non-null uint8
property_type_Other                             5829 non-null uint8
property_type_Serviced apartment                5829 non-null uint8
property_type_Townhouse                         5829 non-null uint8
room_type_Entire home/apt                       5829 non-null uint8
room_type_Private room                          5829 non-null uint8
room_type_Shared room                           5829 non-null uint8
cancellation_flexible                           5829 non-null uint8
cancellation_moderate                           5829 non-null uint8
cancellation_strict                             5829 non-null uint8
cancellation_strict_with_grace_period           5829 non-null uint8
cancellation_super_strict                       5829 non-null uint8
dtypes: float64(16), int32(2), int64(9), object(1), uint8(50)
memory usage: 1.7+ MB

df1count = listings[(listings["host_response_rate"] >= 90) &(listings["host_response_rate"]<=100)].count()
#listings["host_response_rate"] = listings["host_response_rate"].astype(object)
df1 = listings[(listings["host_response_rate"] >= 90) &(listings["host_response_rate"]<=100)]
c1 = df1["host_response_rate"].count()
print(c1)
df2 = listings[(listings["host_response_rate"] < 90) & (listings["host_response_rate"]>=80)]
c2 = df2.host_response_rate.count()
print(c2)
df3 = listings[(listings["host_response_rate"] < 80) & (listings["host_response_rate"]>=70)]
c3 = df3.host_response_rate.count()
print(c3)
df4 = listings[(listings["host_response_rate"] < 70) & (listings["host_response_rate"]>=60)]
c4 = df4.host_response_rate.count()
print(c4)
df5 = listings[(listings["host_response_rate"] < 60)]
c5 = df5.host_response_rate.count()
print(c5)
print(c1+c2+c3+c4+c5)
pivot5 = df5.pivot_table(index= "host_response_rate", values="price", aggfunc='mean')
pivot5 = pivot5.sort_values("price",ascending=False)

sns.boxplot(df5["host_response_rate"])
plt.show()
pivot5

5357
239
58
61
114
5829

listings["host_response_rate"] = np.where(((listings['host_response_rate'] >=90) & (listings['host_response_rate'] <=100)), 95, listings['host_response_rate'])
listings["host_response_rate"] = np.where(((listings['host_response_rate'] >=80) & (listings['host_response_rate'] <90)), 85, listings['host_response_rate'])
listings["host_response_rate"] = np.where(((listings['host_response_rate'] >=70) & (listings['host_response_rate'] <80)), 75, listings['host_response_rate'])
listings["host_response_rate"] = np.where(((listings['host_response_rate'] >=60) & (listings['host_response_rate'] <70)), 65, listings['host_response_rate'])
listings["host_response_rate"] = np.where((listings['host_response_rate'] <60), 50, listings['host_response_rate'])
listings["host_response_rate"] = listings["host_response_rate"].astype(object)
listings["host_response_rate"] = listings["host_response_rate"].map({ 95.0 : '90-100',85.0 : '80-90', 75.0 : '70-80',65.0: '60-70',50.0:'<60'})

host_response_rate = pd.get_dummies(listings["host_response_rate"],columns = "host_response_rate", prefix = "host_response_rate")
listings = pd.concat([listings, host_response_rate], axis=1)
listings.drop(['host_response_rate'],axis=1,inplace= True)
listings

listings = listings[listings["bedrooms"] <= 5]

print(listings.bedrooms.count())
pivot5 = listings.pivot_table(index= "bedrooms", values="price", aggfunc='mean')
pivot5 = pivot5.sort_values("price",ascending=False)
pivot5.plot(kind='bar', color='pink')
plt.show()
pivot5

5829

bedrooms = pd.get_dummies(listings["bedrooms"],columns = "bedrooms", prefix = "bedrooms")
listings = pd.concat([listings, bedrooms], axis=1)
listings.drop(['bedrooms'],axis=1,inplace= True)
listings

listings = listings[listings["bathrooms"] <= 5]

print(listings.bathrooms.count())
pivot6 = listings.pivot_table(index= "bathrooms", values="price", aggfunc='mean')

pivot6.plot(kind='bar', color='pink')
plt.show()
pivot6

5792

bathrooms = pd.get_dummies(listings["bathrooms"],columns = "bathrooms", prefix = "bathrooms")
listings = pd.concat([listings, bathrooms], axis=1)
listings.drop(['bathrooms'],axis=1,inplace= True)
listings

listings = listings[listings["beds"] <= 6]
listings["beds"] = listings["beds"].astype(int)
print(listings.beds.count())
pivot8 = listings.pivot_table(index= "beds", values="price", aggfunc='mean')
pivot8 = pivot8.sort_values("price",ascending=False)
pivot8.plot(kind='bar', color='pink')
plt.show()
pivot8

5774

pivot7 = listings.pivot_table(index = "accommodates", values ="price", aggfunc ='mean')
pivot7 = pivot7.sort_values("price",ascending = False)
pivot7.plot(kind ='bar', color ='pink')
plt.show()
pivot7

pivot9 = listings.pivot_table(index = "guests_included", values ="price", aggfunc ='mean')
pivot9 = pivot9.sort_values("price",ascending = False)
pivot9.plot(kind ='bar', color ='pink')
plt.show()
pivot9

listings.info()

<class 'pandas.core.frame.DataFrame'>
Int64Index: 5774 entries, 0 to 7832
Data columns (total 96 columns):
host_is_superhost                               5774 non-null int32
host_identity_verified                          5774 non-null object
is_location_exact                               5774 non-null int32
accommodates                                    5774 non-null int64
beds                                            5774 non-null int32
price                                           5774 non-null float64
security_deposit                                5774 non-null float64
cleaning_fee                                    5774 non-null float64
guests_included                                 5774 non-null int64
extra_people                                    5774 non-null float64
minimum_nights                                  5774 non-null int64
maximum_nights                                  5774 non-null int64
number_of_reviews                               5774 non-null int64
review_scores_rating                            5774 non-null float64
review_scores_accuracy                          5774 non-null float64
review_scores_cleanliness                       5774 non-null float64
review_scores_checkin                           5774 non-null float64
review_scores_communication                     5774 non-null float64
review_scores_location                          5774 non-null float64
review_scores_value                             5774 non-null float64
calculated_host_listings_count                  5774 non-null int64
calculated_host_listings_count_entire_homes     5774 non-null int64
calculated_host_listings_count_private_rooms    5774 non-null int64
calculated_host_listings_count_shared_rooms     5774 non-null int64
reviews_per_month                               5774 non-null float64
host_response_time_a few days or more           5774 non-null uint8
host_response_time_within a day                 5774 non-null uint8
host_response_time_within a few hours           5774 non-null uint8
host_response_time_within an hour               5774 non-null uint8
neighbourhood_Bayview                           5774 non-null uint8
neighbourhood_Bernal Heights                    5774 non-null uint8
neighbourhood_Castro/Upper Market               5774 non-null uint8
neighbourhood_Chinatown                         5774 non-null uint8
neighbourhood_Downtown/Civic Center             5774 non-null uint8
neighbourhood_Excelsior                         5774 non-null uint8
neighbourhood_Financial District                5774 non-null uint8
neighbourhood_Haight Ashbury                    5774 non-null uint8
neighbourhood_Inner Richmond                    5774 non-null uint8
neighbourhood_Inner Sunset                      5774 non-null uint8
neighbourhood_Marina                            5774 non-null uint8
neighbourhood_Mission                           5774 non-null uint8
neighbourhood_Nob Hill                          5774 non-null uint8
neighbourhood_Noe Valley                        5774 non-null uint8
neighbourhood_North Beach                       5774 non-null uint8
neighbourhood_Ocean View                        5774 non-null uint8
neighbourhood_Other                             5774 non-null uint8
neighbourhood_Outer Mission                     5774 non-null uint8
neighbourhood_Outer Richmond                    5774 non-null uint8
neighbourhood_Outer Sunset                      5774 non-null uint8
neighbourhood_Pacific Heights                   5774 non-null uint8
neighbourhood_Parkside                          5774 non-null uint8
neighbourhood_Potrero Hill                      5774 non-null uint8
neighbourhood_Russian Hill                      5774 non-null uint8
neighbourhood_South of Market                   5774 non-null uint8
neighbourhood_Visitacion Valley                 5774 non-null uint8
neighbourhood_West of Twin Peaks                5774 non-null uint8
neighbourhood_Western Addition                  5774 non-null uint8
property_type_Apartment                         5774 non-null uint8
property_type_Boutique hotel                    5774 non-null uint8
property_type_Condominium                       5774 non-null uint8
property_type_Guest suite                       5774 non-null uint8
property_type_Hostel                            5774 non-null uint8
property_type_Hotel                             5774 non-null uint8
property_type_House                             5774 non-null uint8
property_type_Other                             5774 non-null uint8
property_type_Serviced apartment                5774 non-null uint8
property_type_Townhouse                         5774 non-null uint8
room_type_Entire home/apt                       5774 non-null uint8
room_type_Private room                          5774 non-null uint8
room_type_Shared room                           5774 non-null uint8
cancellation_flexible                           5774 non-null uint8
cancellation_moderate                           5774 non-null uint8
cancellation_strict                             5774 non-null uint8
cancellation_strict_with_grace_period           5774 non-null uint8
cancellation_super_strict                       5774 non-null uint8
host_response_rate_60-70                        5774 non-null uint8
host_response_rate_70-80                        5774 non-null uint8
host_response_rate_80-90                        5774 non-null uint8
host_response_rate_90-100                       5774 non-null uint8
host_response_rate_<60                          5774 non-null uint8
bedrooms_1.0                                    5774 non-null uint8
bedrooms_2.0                                    5774 non-null uint8
bedrooms_3.0                                    5774 non-null uint8
bedrooms_4.0                                    5774 non-null uint8
bedrooms_5.0                                    5774 non-null uint8
bathrooms_0.0                                   5774 non-null uint8
bathrooms_0.5                                   5774 non-null uint8
bathrooms_1.0                                   5774 non-null uint8
bathrooms_1.5                                   5774 non-null uint8
bathrooms_2.0                                   5774 non-null uint8
bathrooms_2.5                                   5774 non-null uint8
bathrooms_3.0                                   5774 non-null uint8
bathrooms_3.5                                   5774 non-null uint8
bathrooms_4.0                                   5774 non-null uint8
bathrooms_4.5                                   5774 non-null uint8
bathrooms_5.0                                   5774 non-null uint8
dtypes: float64(12), int32(3), int64(9), object(1), uint8(71)
memory usage: 1.5+ MB

FEATURE SCALING¶

listings.drop(columns = ["host_identity_verified"],inplace = True)

y = listings["price"]
X = listings.drop(columns = ["price"])

X_train_org,X_test_org,y_train,y_test=train_test_split(X,y,random_state=0,test_size=0.3)

X_train_org.shape

(4041, 94)

X_test_org.shape

(1733, 94)

sc = StandardScaler()
X_train = sc.fit_transform(X_train_org)
X_test = sc.transform(X_test_org)

MODELING¶

LINEAR REGRESSION¶

train_score = []
test_score = []
model_Scores = []

from sklearn.linear_model import LinearRegression

lreg = LinearRegression()
lreg.fit(X_train, y_train)
print('Train Score:',lreg.score(X_train, y_train))
train_score.append(lreg.score(X_train, y_train))
test_score.append(lreg.score(X_test, y_test))
train_score = lreg.score(X_train, y_train)
test_score = lreg.score(X_test,y_test)
print("Test Score {:.2f}".format(test_score))
## Appending the results 
model_Scores.append({'Model Type' : 'Regression',
                    'Model Name' : 'Linear Regression',
                    'Parameters' : '-',
                    'Training_score': train_score,
                    'Test Score':test_score})

Train Score: 0.6277386146120525
Test Score 0.57

y_pred_lin = lreg.predict(X_test)
r2_score(y_test,y_pred_lin)

0.5746322172239109

%matplotlib inline
import matplotlib.pyplot as plt

X_train_rm = X_train[:,5].reshape(-1,1)
lreg.fit(X_train_rm, y_train)
y_predict = lreg.predict(X_train_rm)

plt.plot(X_train_rm, y_predict, c = 'r')
plt.scatter(X_train_rm,y_train)
plt.xlabel('RM')

Text(0.5, 0, 'RM')

from sklearn import model_selection
kfold = model_selection.KFold(n_splits = 10, random_state = 100)
model_kfold = LinearRegression()
results_kfold = model_selection.cross_val_score(model_kfold, X, y, cv = kfold)
print("Accuracy: %.2f%%" % (results_kfold.mean()*100.0))

Accuracy: 58.00%

POLYNOMIAL REGRESSION¶

from  sklearn.preprocessing  import PolynomialFeatures

X_train_1 = X_train[:,5].reshape(-1,1)
plt.scatter(X_train_1,y_train)

<matplotlib.collections.PathCollection at 0x28091c8c6d8>

train_score_list = []
test_score_list = []

for n in range(2,4):
    poly = PolynomialFeatures(n)
    X_train_poly = poly.fit_transform(X_train)
    X_test_poly = poly.transform(X_test)
    lreg.fit(X_train_poly, y_train)
    scores_train = cross_val_score(lreg, X_train_poly, y_train, cv=3)
    scores_test = lreg.score(X_test_poly,y_test)
    test_score_list.append(scores_test)
    train_score_list.append(scores_train.mean())
    
print("Train Scores", train_score_list)
print("Test Scores", test_score_list)

Train Scores [-3.5488865482143527e+19, -9.918478435232078e+18]
Test Scores [-1.137344328040649e+20, -1.365666205564552e+18]

print("Train Scores", train_score_list[1])
print("Test Scores", test_score_list[1])
model_Scores.append({'Model Type' : 'Regression',
                    'Model Name' : 'Polynomial Regression',
                    'Parameters' : {'Degree':2},
                    'Training_score':train_score_list[1],
                    'Test Score':test_score_list[1]})

Train Scores -9.918478435232078e+18
Test Scores -1.365666205564552e+18

%matplotlib inline

x_axis = range(1,3)
plt.plot(x_axis, train_score_list, c = 'g', label = 'Train Score')
plt.plot(x_axis, test_score_list, c = 'b', label = 'Test Score')
plt.xlabel('degree')
plt.ylabel('accuracy')
plt.legend()

<matplotlib.legend.Legend at 0x28090944588>

poly = PolynomialFeatures(n)
X_train_poly = poly.fit_transform(X_train_1)
lreg.fit(X_train_poly, y_train)

x_axis = np.linspace(0,1,100).reshape(-1,1)
x_poly = poly.transform(x_axis)
y_predict = lreg.predict(x_poly)

X_train_1 = X_train[:,5].reshape(-1,1)
plt.scatter(X_train_1,y_train)
plt.plot(x_axis, y_predict, c = 'r')

[<matplotlib.lines.Line2D at 0x280907d90b8>]

KNN REGRESSOR¶

%matplotlib inline
train_score_array = []
test_score_array = []

for k in range(1,10):
    knn_reg = KNeighborsRegressor(k)
    knn_reg.fit(X_train, y_train)
    train_score_array.append(knn_reg.score(X_train, y_train))
    test_score_array.append(knn_reg.score(X_test, y_test))

x_axis = range(1,10)
plt.plot(x_axis, train_score_array, c = 'g', label = 'Train Score')
plt.plot(x_axis, test_score_array, c = 'b', label = 'Test Score')
plt.legend()
plt.xlabel('k')
plt.ylabel('MSE')

Text(0, 0.5, 'MSE')

print(train_score_array)
print(test_score_array)

[0.9866769627505549, 0.831114173371621, 0.7714883635478882, 0.7318915154382359, 0.7083850723669651, 0.6863813940484661, 0.6728232957439768, 0.6619156474613307, 0.6491471631361726]
[0.26151741569745335, 0.4367007563309244, 0.47284959831773177, 0.49254423737279684, 0.5000169797987188, 0.49444284094157265, 0.49760948274636224, 0.5007786974985815, 0.5084147139095303]

def printGridResult (model_def) :
    print("Best CV result: {}".format(model_def.best_score_))
    print("Best parameters: {}".format(model_def.best_params_))
    print("Training Score: {}".format(model_def.score(X_train, y_train)))
    print("Testing Score: {}".format(model_def.score(X_test, y_test)))
    
#KNN Regressor
knnRegressor = KNeighborsRegressor()
noofNeighbors = {'n_neighbors':[1, 5, 10, 15, 20]}
print("Parameter grid:\n{}".format(noofNeighbors))
knnGridSCV = GridSearchCV(knnRegressor, param_grid = noofNeighbors, cv=5, return_train_score=True)
knnGridSCV.fit(X_train, y_train)

n_neighbors = [1, 5, 10, 15, 20]

n_neighbors = [1, 5, 10, 15, 20]

train_knnRegressor = knnGridSCV.cv_results_["mean_train_score"]

test_knnRegressor = knnGridSCV.cv_results_["mean_test_score"]

printGridResult(knnGridSCV)

model_Scores.append({'Model Type' : 'Regression',
                    'Model Name' : 'KNN Regressor',
                    'Parameters' : knnGridSCV.best_params_,
                    'Training_score': knnGridSCV.score(X_train, y_train),
                    'Test Score': knnGridSCV.score(X_test, y_test )})

Parameter grid:
{'n_neighbors': [1, 5, 10, 15, 20]}
Best CV result: 0.5284313086577018
Best parameters: {'n_neighbors': 10}
Training Score: 0.6393939518337186
Testing Score: 0.5103967496168658

X_b = X_train[:50,6].reshape(-1,1)
y_b = y_train[:50]

knn_reg = KNeighborsRegressor(10)
knn_reg.fit(X_b, y_b)

X_new=np.linspace(X_b.min(), X_b.max(), 50).reshape(50, 1)
y_predict = knn_reg.predict(X_new)

plt.plot(X_new, y_predict, c = 'r')
plt.scatter(X_b, y_b)

<matplotlib.collections.PathCollection at 0x28091cd0be0>

RIDGE REGRESSION¶

from  sklearn.linear_model import Ridge

x_range = [0.01, 0.1, 1, 10, 100]
train_score_list = []
test_score_list = []

for alpha in x_range: 
    ridge = Ridge(alpha)
    ridge.fit(X_train,y_train)
    train_score_list.append(ridge.score(X_train,y_train))
    test_score_list.append(ridge.score(X_test, y_test))
print(train_score_list)
print(test_score_list)

[0.6278141770666759, 0.627814176831849, 0.6278141533832768, 0.6278118419880367, 0.6276089028868538]
[0.5746801892281622, 0.5746807119848855, 0.5746859153486843, 0.5747355677606556, 0.5750267353139064]

%matplotlib inline
import matplotlib.pyplot as plt
plt.plot(x_range, train_score_list, c = 'g', label = 'Train Score')
plt.plot(x_range, test_score_list, c = 'b', label = 'Test Score')
plt.xscale('log')
plt.legend(loc = 3)
plt.xlabel(r'$\alpha$')

Text(0.5, 0, '$\\alpha$')

# Ridge Regression
ridgeParamGSV = {'alpha': [0.001, 0.01, 0.1, 1, 10, 100]}
print("Parameter grid:\n{}".format(ridgeParamGSV))

ridgeGridSV = GridSearchCV(estimator = Ridge(random_state = 0 ),param_grid = ridgeParamGSV, 
                           cv=5, return_train_score=True, scoring='r2', n_jobs=1)
ridgeGridSV.fit(X_train, y_train)

ridge_train_scores_mean = ridgeGridSV.cv_results_["mean_train_score"]
ridge_test_scores_mean = ridgeGridSV.cv_results_["mean_test_score"]

model_Scores.append({'Model Type' : 'Regression',
                    'Model Name' : 'Ridge Regression',
                    'Parameters' : ridgeGridSV.best_params_,
                    'Training_score': ridgeGridSV.score(X_train, y_train),
                    'Test Score': ridgeGridSV.score(X_test, y_test )})
printGridResult(ridgeGridSV)

Parameter grid:
{'alpha': [0.001, 0.01, 0.1, 1, 10, 100]}
Best CV result: 0.6033043158737826
Best parameters: {'alpha': 100}
Training Score: 0.6276089028868538
Testing Score: 0.5750267353139064

%matplotlib inline
import numpy as np

x_range1 = np.linspace(0.001, 1, 100).reshape(-1,1)
x_range2 = np.linspace(1, 10000, 10000).reshape(-1,1)

x_range = np.append(x_range1, x_range2)
coeff = []

for alpha in x_range: 
    ridge = Ridge(alpha)
    ridge.fit(X_train,y_train)
    coeff.append(ridge.coef_ )
    
coeff = np.array(coeff)

for i in range(0,20):
    plt.plot(x_range, coeff[:,i], label = 'feature {:d}'.format(i))

plt.axhline(y=0, xmin=0.001, xmax=9999, linewidth=1, c ='gray')
plt.xlabel(r'$\alpha$')
plt.xscale('log')
plt.legend(loc='upper center', bbox_to_anchor=(0.5, 1.5),
          ncol=3, fancybox=True, shadow=True)
plt.show()

from sklearn import model_selection
kfold = model_selection.KFold(n_splits = 10, random_state = 100)
model_kfold = Ridge(alpha = 100)
results_kfold = model_selection.cross_val_score(model_kfold, X, y, cv=kfold)
print("Accuracy: %.2f%%" %(results_kfold.mean()*100.0))

Accuracy: 56.92%

LASSO REGRESSION¶

from sklearn.linear_model import Lasso
x_range = [0.01, 0.1, 1, 10, 100]
train_score_list = []
test_score_list = []

for alpha in x_range: 
    lasso = Lasso(alpha)
    lasso.fit(X_train,y_train)
    train_score_list.append(lasso.score(X_train,y_train))
    test_score_list.append(lasso.score(X_test, y_test))
print(train_score_list)
print(test_score_list)

[0.6278130082260489, 0.6277062421084709, 0.6196471453062028, 0.47360413399162643, 0.0]
[0.5746989247324958, 0.5747415866899004, 0.5691794137116017, 0.44042142009653773, -0.001725493328951444]

#Lasso Regression
lassoParamGSV = {'alpha': [0.001, 0.01, 0.1, 1, 10, 100]}
print("Parameter grid:\n{}".format(lassoParamGSV))

lassoGridSV = GridSearchCV(estimator = Lasso(random_state = 0 ),param_grid = lassoParamGSV, 
                           cv=5, return_train_score=True, scoring='r2', n_jobs=1)
lassoGridSV.fit(X_train, y_train)

lasso_train_scores_mean = lassoGridSV.cv_results_["mean_train_score"]
lasso_test_scores_mean = lassoGridSV.cv_results_["mean_test_score"]

model_Scores.append({'Model Type' : 'Regression',
                    'Model Name' : 'Lasso Regression',
                    'Parameters' : lassoGridSV.best_params_,
                    'Training_score': lassoGridSV.score(X_train, y_train),
                    'Test Score': lassoGridSV.score(X_test, y_test )})
printGridResult(lassoGridSV)

Parameter grid:
{'alpha': [0.001, 0.01, 0.1, 1, 10, 100]}
Best CV result: 0.6026840709968957
Best parameters: {'alpha': 0.1}
Training Score: 0.6277062421084709
Testing Score: 0.5747415866899004

%matplotlib inline

x_range1 = np.linspace(0.001, 1, 1000).reshape(-1,1)
x_range2 = np.linspace(1, 1000, 1000).reshape(-1,1)

x_range = np.append(x_range1, x_range2)
coeff = []

for alpha in x_range: 
    lasso = Lasso(alpha)
    lasso.fit(X_train,y_train)
    coeff.append(lasso.coef_ )
    
coeff = np.array(coeff)

for i in range(0,20):
    plt.plot(x_range, coeff[:,i], label = 'feature {:d}'.format(i))

plt.axhline(y=0, xmin=0.001, xmax=9999, linewidth=1, c ='gray')
plt.xlabel(r'$\alpha$')
plt.xscale('log')
plt.legend(loc='upper center', bbox_to_anchor=(0.5, 1.5),
          ncol=3, fancybox=True, shadow=True)
plt.show()

from sklearn import model_selection
kfold = model_selection.KFold(n_splits = 10, random_state = 100)
model_kfold = Lasso(alpha = 0.1)
results_kfold = model_selection.cross_val_score(model_kfold, X, y, cv=kfold)
print("Accuracy: %.2f%%" %(results_kfold.mean()*100.0))

Accuracy: 58.01%

SVM REGRESSION¶

from sklearn import svm
tuning_parameters = [{'C': [0.01, 0.1 ,1, 10, 100], 'kernel': ['linear']},
  {'C': [0.01, 0.1 ,1, 10, 100], 'gamma': [0.1,0.01, 0.001, 0.0001, 0.00001], 'kernel': ['rbf']}]

svm_grid = GridSearchCV(svm.SVR(), param_grid=tuning_parameters,cv=5)


svm_model=svm_grid.fit(X_train,y_train) 
print(svm_model.best_params_)
print('Validation score:', svm_model.best_score_)

{'C': 100, 'gamma': 0.01, 'kernel': 'rbf'}
Validation score: 0.634296648179041

#SVM With Linear Kernal
from sklearn.svm import LinearSVR,SVR
linearSVM = SVR(kernel='linear')
param_Linear_SVM = { 
    'gamma': [0.001, 0.01, 0.1, 1, 10],
    'C': [0.001, 0.01, 0.1, 1, 10],
}

LinearSVMGrid = GridSearchCV(estimator=linearSVM, param_grid=param_Linear_SVM, cv=3, n_jobs=-1)
LinearSVMGrid.fit(X_train, y_train)

print('Train score:',LinearSVMGrid.score(X_train, y_train))
print('Test score:',LinearSVMGrid.score(X_test, y_test))
print('Best parameters:',LinearSVMGrid.best_params_)

model_Scores.append({'Model Type' : 'Regression',
                    'Model Name' : 'Linear SVM',
                    'Parameters' : LinearSVMGrid.best_params_,
                    'Training_score': LinearSVMGrid.score(X_train, y_train),
                    'Test Score': LinearSVMGrid.score(X_test, y_test )})

Train score: 0.608152859484705
Test score: 0.5527286103744447
Best parameters: {'C': 1, 'gamma': 0.001}

#SVM With Radial Kernal
svmRadial = SVR(kernel = 'rbf')
param_Radial_SVM = {'C': [0.001, 0.01, 0.1, 1, 10, 100],'gamma': [0.001, 0.01, 0.1, 1, 10, 100]}
print("Parameter grid:\n{}".format(param_Radial_SVM))
svmRadialGridSV  = GridSearchCV(svmRadial, param_grid = param_Radial_SVM, cv=5, return_train_score=True)

svmRadialGridSV.fit(X_train, y_train)

svmRadialGridSV_results = pd.DataFrame(svmRadialGridSV.cv_results_)

printGridResult(svmRadialGridSV)
model_Scores.append({'Model Type' : 'Regression',
                    'Model Name' : 'SVM Radial',
                    'Parameters' : svmRadialGridSV.best_params_,
                    'Training_score': svmRadialGridSV.score(X_train, y_train),
                    'Test Score': svmRadialGridSV.score(X_test, y_test )})

Parameter grid:
{'C': [0.001, 0.01, 0.1, 1, 10, 100], 'gamma': [0.001, 0.01, 0.1, 1, 10, 100]}
Best CV result: 0.634296648179041
Best parameters: {'C': 100, 'gamma': 0.01}
Training Score: 0.7965109389825114
Testing Score: 0.6129452123567071

#Polynomial SVM Kernal
polySVM = SVR(kernel = 'poly', degree = 2)
paramPolySVM = {'C': [0.001, 0.01, 0.1, 1, 10, 100]}
print("Parameter grid:\n{}".format(paramPolySVM))
polySVMGridSV = GridSearchCV(polySVM, param_grid = paramPolySVM, cv=5, return_train_score= True)

polySVMGridSV.fit(X_train, y_train)

printGridResult(polySVMGridSV)
model_Scores.append({'Model Type' : 'Regression',
                    'Model Name' : 'Polynomial SVM',
                    'Parameters' : polySVMGridSV.best_params_,
                    'Training_score': polySVMGridSV.score(X_train, y_train),
                    'Test Score': polySVMGridSV.score(X_test, y_test )})

Parameter grid:
{'C': [0.001, 0.01, 0.1, 1, 10, 100]}
Best CV result: 0.547785744088905
Best parameters: {'C': 100}
Training Score: 0.7464654145254829
Testing Score: 0.540805998063183

from sklearn.metrics import mean_squared_error
import xgboost as xgb
import time
from xgboost.sklearn import XGBRegressor

xgb_reg_start = time.time()

xgb_reg = xgb.XGBRegressor()
xgb_reg.fit(X_train, y_train)
training_preds_xgb_reg = xgb_reg.predict(X_train)
val_preds_xgb_reg = xgb_reg.predict(X_test)

xgb_reg_end = time.time()

print(f"Time taken to run: {round((xgb_reg_end - xgb_reg_start)/60,1)} minutes")
print("\nTraining MSE:", round(mean_squared_error(y_train, training_preds_xgb_reg),4))
print("Validation MSE:", round(mean_squared_error(y_test, val_preds_xgb_reg),4))
print("\nTraining r2:", round(r2_score(y_train, training_preds_xgb_reg),4))
model_Scores.append({'Model Type' : 'Regression',
                    'Model Name' : 'XGBRegressor',
                    'Parameters' : '-',
                    'Training_score': xgb_reg.score(X_train, y_train),
                    'Test Score': xgb_reg.score(X_test, y_test )})

[17:36:01] WARNING: C:/Jenkins/workspace/xgboost-win64_release_0.90/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.
Time taken to run: 0.0 minutes

Training MSE: 2695.2386
Validation MSE: 3490.6037

Training r2: 0.7172

feature_important = xgb_reg.get_booster().get_score(importance_type='weight')
keys = list(feature_important.keys())
values = list(feature_important.values())

data = pd.DataFrame(data=values, index=keys, columns=["score"]).sort_values(by = "score", ascending=True)
print('Feature Importance using XGBRegressor Model')
data.plot(kind='barh',figsize=(30,30))

Feature Importance using XGBRegressor Model

<matplotlib.axes._subplots.AxesSubplot at 0x280906e6b70>

From our best Model i.e. the XGBRegressor Model; we found that the following 10 features are important to determine the price of a listing:

minimum_nights

cleaning_fee

accommodates

security_deposit

guests_included

reviews_per_month

extra_people

number_of_reviews

calculated_host_listings_count

review_scores_rating

Model Results¶

print("Model Results:")
modelResult = pd.DataFrame(model_Scores)
# Dropped the duplicate column 'Best Parameters'as we already have Parameters displayed in the Result grid
modelResult.drop('Best Parameters', axis = 1, inplace = True)
modelResult.set_index('Model Name', inplace = True)
modelResult

Model Results:

From the above results obtained by comparing the various ML models; XGB Regressor is the best performing model in our case based on the Training and Test scores.

	host_response_rate	accommodates	bathrooms	bedrooms	beds	price	security_deposit	cleaning_fee	guests_included	extra_people	minimum_nights	maximum_nights	number_of_reviews	review_scores_rating	review_scores_accuracy	review_scores_cleanliness	review_scores_checkin	review_scores_communication	review_scores_location	review_scores_value	calculated_host_listings_count	calculated_host_listings_count_entire_homes	calculated_host_listings_count_private_rooms	calculated_host_listings_count_shared_rooms	reviews_per_month	host_response_time_a few days or more	host_response_time_within a day	host_response_time_within a few hours	host_response_time_within an hour
count	5829.000000	5829.000000	5829.000000	5829.000000	5829.000000	5829.000000	5829.000000	5829.000000	5829.000000	5829.000000	5829.000000	5829.000000	5829.000000	5829.000000	5829.000000	5829.000000	5829.000000	5829.000000	5829.000000	5829.000000	5829.000000	5829.000000	5829.000000	5829.000000	5829.000000	5829.000000	5829.000000	5829.000000	5829.000000
mean	96.415337	3.049065	1.392520	1.404186	1.716589	172.285126	355.790702	95.297307	1.708012	17.317722	15.573512	534.487734	51.167096	95.584447	9.762229	9.613503	9.858173	9.833519	9.692983	9.400051	22.260937	18.505404	2.995368	0.760165	1.686802	0.010122	0.113399	0.200892	0.675588
std	11.363627	1.743503	0.940483	0.716955	1.071228	98.668038	652.495125	84.261654	1.230129	29.079051	20.150522	585.417253	81.964469	6.352995	0.645156	0.743256	0.484629	0.555494	0.598023	0.754626	55.685452	54.446679	6.002043	3.518524	2.058539	0.100105	0.317107	0.400702	0.468195
min	0.000000	1.000000	0.000000	1.000000	1.000000	10.000000	0.000000	0.000000	1.000000	0.000000	1.000000	1.000000	0.000000	20.000000	2.000000	2.000000	2.000000	2.000000	2.000000	2.000000	1.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000
25%	100.000000	2.000000	1.000000	1.000000	1.000000	99.000000	0.000000	35.000000	1.000000	0.000000	2.000000	29.000000	1.000000	95.000000	9.767117	9.625100	9.859325	9.835958	9.686174	9.000000	1.000000	0.000000	0.000000	0.000000	0.120000	0.000000	0.000000	0.000000	0.000000
50%	100.000000	2.000000	1.000000	1.000000	1.000000	150.000000	150.000000	80.000000	1.000000	5.000000	4.000000	180.000000	15.000000	96.000000	10.000000	10.000000	10.000000	10.000000	10.000000	9.403859	2.000000	1.000000	1.000000	0.000000	0.840000	0.000000	0.000000	0.000000	1.000000
75%	100.000000	4.000000	1.500000	2.000000	2.000000	228.000000	500.000000	125.000000	2.000000	25.000000	30.000000	1125.000000	68.000000	99.000000	10.000000	10.000000	10.000000	10.000000	10.000000	10.000000	7.000000	2.000000	3.000000	0.000000	2.660000	0.000000	0.000000	0.000000	1.000000
max	100.000000	15.000000	10.000000	5.000000	15.000000	499.000000	5100.000000	663.000000	10.000000	300.000000	365.000000	10000.000000	730.000000	100.000000	10.000000	10.000000	10.000000	10.000000	10.000000	10.000000	254.000000	254.000000	43.000000	25.000000	14.060000	1.000000	1.000000	1.000000	1.000000

	price
host_response_rate
10.0	475.000000
56.0	314.000000
40.0	280.666667
55.0	244.000000
33.0	200.000000
44.0	200.000000
0.0	197.162162
43.0	174.500000
50.0	164.968750
29.0	146.666667
30.0	143.888889
25.0	125.000000
57.0	112.000000
53.0	80.250000
51.0	44.000000

	price
beds
6	328.896552
5	298.608696
4	259.528369
3	247.833922
2	203.577483
1	133.409105

	price
accommodates
12	412.714286
10	339.842105
8	334.318182
9	302.833333
11	300.000000
15	300.000000
7	299.794521
6	280.410891
5	241.790698
4	218.374264
3	156.142857
2	140.065226
1	84.732673

	price
guests_included
10	441.666667
8	370.882353
9	329.000000
6	311.823009
5	272.610169
7	265.500000
4	260.479303
3	213.369427
2	170.678859
1	153.064085

	id	listing_url	scrape_id	last_scraped	name	summary	space	description	experiences_offered	neighborhood_overview	notes	transit	access	interaction	house_rules	thumbnail_url	medium_url	picture_url	xl_picture_url	host_id	host_url	host_name	host_since	host_location	host_about	host_response_time	host_response_rate	host_acceptance_rate	host_is_superhost	host_thumbnail_url	host_picture_url	host_neighbourhood	host_listings_count	host_total_listings_count	host_verifications	host_has_profile_pic	host_identity_verified	street	neighbourhood	neighbourhood_cleansed	neighbourhood_group_cleansed	city	state	zipcode	market	smart_location	country_code	country	latitude	longitude	is_location_exact	property_type	room_type	accommodates	bathrooms	bedrooms	beds	bed_type	amenities	square_feet	price	weekly_price	monthly_price	security_deposit	cleaning_fee	guests_included	extra_people	minimum_nights	maximum_nights	minimum_minimum_nights	maximum_minimum_nights	minimum_maximum_nights	maximum_maximum_nights	minimum_nights_avg_ntm	maximum_nights_avg_ntm	calendar_updated	has_availability	availability_30	availability_60	availability_90	availability_365	calendar_last_scraped	number_of_reviews	number_of_reviews_ltm	first_review	last_review	review_scores_rating	review_scores_accuracy	review_scores_cleanliness	review_scores_checkin	review_scores_communication	review_scores_location	review_scores_value	requires_license	license	jurisdiction_names	instant_bookable	is_business_travel_ready	cancellation_policy	require_guest_profile_picture	require_guest_phone_verification	calculated_host_listings_count	calculated_host_listings_count_entire_homes	calculated_host_listings_count_private_rooms	reviews_per_month
0	958	https://www.airbnb.com/rooms/958	20190806030527	2019-08-06	Bright, Modern Garden Unit - 1BR/1B	New update: the house next door is under const...	Newly remodeled, modern, and bright garden uni...	New update: the house next door is under const...	none	Quiet cul de sac in friendly neighborhood St...	Due to the fact that we have children and a do...	Public Transportation is 1/2 block away. Ce...	*Full access to patio and backyard (shared wit...	A family of 4 lives upstairs with their dog. N...	* No Pets - even visiting guests for a short t...	NaN	NaN	https://a0.muscache.com/im/pictures/b7c2a199-4...	NaN	1169	https://www.airbnb.com/users/show/1169	Holly	2008-07-31	San Francisco, California, United States	We are a family with 2 boys born in 2009 and 2...	within an hour	90%	NaN	t	https://a0.muscache.com/im/pictures/efdad96a-3...	https://a0.muscache.com/im/pictures/efdad96a-3...	Duboce Triangle	1.0	1.0	['email', 'phone', 'facebook', 'reviews', 'kba']	t	t	San Francisco, CA, United States	Lower Haight	Western Addition	NaN	San Francisco	CA	94117	San Francisco	San Francisco, CA	US	United States	37.76931	-122.43386	t	Apartment	Entire home/apt	3	1.0	1.0	2.0	Real Bed	{TV,"Cable TV",Internet,Wifi,Kitchen,"Pets liv...	NaN	$170.00	$1,120.00	$4,200.00	$100.00	$100.00	2	$25.00	1	30	1	1	30	30	1.0	30.0	today	t	1	2	7	115	2019-08-06	199	46	2009-07-23	2019-07-19	97.0	10.0	10.0	10.0	10.0	10.0	9.0	t	STR-0001256	{"SAN FRANCISCO"}	t	f	moderate	f	f	1	1	0	1.63
1	3850	https://www.airbnb.com/rooms/3850	20190806030527	2019-08-06	Charming room for two	Your own private room plus access to a shared ...	This is the first room I rented on Airbnb, sta...	Your own private room plus access to a shared ...	none	This is a quiet, safe neighborhood on a substa...	Here is the complete list of House Rules you a...	Public transit service to my house is outstand...	During the hours of 7:00 to 22:00, your access...	I live at the house in my own bedroom, and I w...	These House Rules have money associated with t...	NaN	NaN	https://a0.muscache.com/im/pictures/b6e97d2a-1...	NaN	4921	https://www.airbnb.com/users/show/4921	Kevin	2008-12-08	San Francisco, California, United States	I am a retired software entrepreneur now raisi...	within an hour	100%	NaN	t	https://a0.muscache.com/im/pictures/97d29791-d...	https://a0.muscache.com/im/pictures/97d29791-d...	Inner Sunset	2.0	2.0	['email', 'phone', 'reviews', 'jumio', 'govern...	t	t	San Francisco, CA, United States	Inner Sunset	Inner Sunset	NaN	San Francisco	CA	94131	San Francisco	San Francisco, CA	US	United States	37.75402	-122.45805	t	House	Private room	2	1.0	1.0	1.0	Real Bed	{Internet,Wifi,Kitchen,Breakfast,"Free street ...	NaN	$99.00	NaN	NaN	$0.00	$10.00	2	$20.00	1	5	1	1	5	5	1.0	5.0	today	t	17	35	59	59	2019-08-06	136	12	2009-07-16	2019-08-01	94.0	9.0	9.0	10.0	10.0	9.0	9.0	t	STR-0000346	{"SAN FRANCISCO"}	f	f	strict_14_with_grace_period	f	f	3	0	3	1.11
2	5858	https://www.airbnb.com/rooms/5858	20190806030527	2019-08-06	Creative Sanctuary	NaN	We live in a large Victorian house on a quiet ...	We live in a large Victorian house on a quiet ...	none	I love how our neighborhood feels quiet but is...	All the furniture in the house was handmade so...	The train is two blocks away and you can stop ...	Our deck, garden, gourmet kitchen and extensiv...	NaN	Please respect the house, the art work, the fu...	NaN	NaN	https://a0.muscache.com/im/pictures/17714/3a7a...	NaN	8904	https://www.airbnb.com/users/show/8904	Philip And Tania	2009-03-02	San Francisco, California, United States	Philip: English transplant to the Bay Area and...	within a day	67%	NaN	f	https://a0.muscache.com/im/users/8904/profile_...	https://a0.muscache.com/im/users/8904/profile_...	Bernal Heights	2.0	2.0	['email', 'phone', 'reviews', 'kba', 'work_ema...	t	t	San Francisco, CA, United States	Bernal Heights	Bernal Heights	NaN	San Francisco	CA	94110	San Francisco	San Francisco, CA	US	United States	37.74511	-122.42102	t	Apartment	Entire home/apt	5	1.0	2.0	3.0	Real Bed	{Internet,Wifi,Kitchen,Heating,"Family/kid fri...	NaN	$235.00	$1,600.00	$5,500.00	NaN	$100.00	2	$0.00	30	60	30	30	60	60	30.0	60.0	2 months ago	t	0	0	0	0	2019-08-06	111	0	2009-05-03	2017-08-06	98.0	10.0	10.0	10.0	10.0	10.0	9.0	t	NaN	{"SAN FRANCISCO"}	f	f	strict_14_with_grace_period	f	f	1	1	0	0.89
3	7918	https://www.airbnb.com/rooms/7918	20190806030527	2019-08-06	A Friendly Room - UCSF/USF - San Francisco	Nice and good public transportation. 7 minute...	Room rental-sunny view room/sink/Wi Fi (inner ...	Nice and good public transportation. 7 minute...	none	Shopping old town, restaurants, McDonald, Whol...	Please email your picture id with print name (...	N Juda Muni and bus stop. Street parking.	NaN	NaN	No party, No smoking, not for any kinds of smo...	NaN	NaN	https://a0.muscache.com/im/pictures/26356/8030...	NaN	21994	https://www.airbnb.com/users/show/21994	Aaron	2009-06-17	San Francisco, California, United States	7 minutes walk to UCSF. 15 minutes walk to US...	within an hour	100%	NaN	t	https://a0.muscache.com/im/users/21994/profile...	https://a0.muscache.com/im/users/21994/profile...	Cole Valley	10.0	10.0	['email', 'phone', 'reviews', 'jumio', 'govern...	t	t	San Francisco, CA, United States	Cole Valley	Haight Ashbury	NaN	San Francisco	CA	94117	San Francisco	San Francisco, CA	US	United States	37.76669	-122.45250	t	Apartment	Private room	2	4.0	1.0	1.0	Real Bed	{TV,Internet,Wifi,Kitchen,"Free street parking...	NaN	$65.00	$485.00	$1,685.00	$200.00	$50.00	1	$12.00	32	60	32	32	60	60	32.0	60.0	2 weeks ago	t	5	35	65	340	2019-08-06	17	0	2009-08-31	2016-11-21	85.0	8.0	8.0	9.0	9.0	9.0	8.0	t	NaN	{"SAN FRANCISCO"}	f	f	strict_14_with_grace_period	f	f	9	0	9	0.14
4	8142	https://www.airbnb.com/rooms/8142	20190806030527	2019-08-06	Friendly Room Apt. Style -UCSF/USF - San Franc...	Nice and good public transportation. 7 minute...	Room rental Sunny view Rm/Wi-Fi/TV/sink/large ...	Nice and good public transportation. 7 minute...	none	NaN	Please email your picture id with print name (...	N Juda Muni, Bus and UCSF Shuttle. small shopp...	NaN	NaN	no pet no smoke no party inside the building	NaN	NaN	https://a0.muscache.com/im/pictures/27832/3b1f...	NaN	21994	https://www.airbnb.com/users/show/21994	Aaron	2009-06-17	San Francisco, California, United States	7 minutes walk to UCSF. 15 minutes walk to US...	within an hour	100%	NaN	t	https://a0.muscache.com/im/users/21994/profile...	https://a0.muscache.com/im/users/21994/profile...	Cole Valley	10.0	10.0	['email', 'phone', 'reviews', 'jumio', 'govern...	t	t	San Francisco, CA, United States	Cole Valley	Haight Ashbury	NaN	San Francisco	CA	94117	San Francisco	San Francisco, CA	US	United States	37.76487	-122.45183	t	Apartment	Private room	2	4.0	1.0	1.0	Real Bed	{TV,Internet,Wifi,Kitchen,"Free street parking...	NaN	$65.00	$490.00	$1,685.00	$200.00	$50.00	1	$12.00	32	90	32	32	90	90	32.0	90.0	2 weeks ago	t	30	60	90	365	2019-08-06	8	1	2014-09-08	2018-09-12	93.0	9.0	9.0	10.0	10.0	9.0	9.0	t	NaN	{"SAN FRANCISCO"}	f	f	strict_14_with_grace_period	f	f	9	0	9	0.13

	host_response_time_a few days or more	host_response_time_within a day	host_response_time_within a few hours	host_response_time_within an hour
0	0	0	0	1
1	0	0	0	1
2	0	1	0	0
3	0	0	0	1
4	0	0	0	1
5	0	0	1	0
6	0	1	0	0
7	0	0	0	1
8	0	0	0	1
9	0	0	0	1
11	0	0	1	0
12	0	0	1	0
13	0	0	1	0
14	0	0	0	1
15	0	0	0	1
16	0	0	0	1
17	0	1	0	0
19	0	1	0	0
21	0	0	1	0
22	0	0	0	1
23	0	0	0	1
24	0	0	0	1
25	0	0	0	1
26	0	0	0	1
27	0	0	1	0
28	0	1	0	0
29	0	0	0	1
30	0	0	1	0
31	0	0	1	0
33	0	0	0	1
34	0	0	0	1
35	0	1	0	0
36	0	0	0	1
37	0	0	1	0
38	0	1	0	0
39	0	0	0	1
40	0	1	0	0
42	0	0	0	1
43	0	0	1	0
44	0	0	1	0
45	0	0	0	1
48	0	0	0	1
50	0	1	0	0
51	0	0	1	0
52	0	1	0	0
53	0	1	0	0
54	0	0	0	1
55	0	0	1	0
57	0	0	0	1
59	0	0	1	0
62	0	0	0	1
63	0	0	1	0
65	0	0	1	0
67	0	0	1	0
68	0	0	1	0
69	0	1	0	0
70	0	1	0	0
72	0	0	1	0
73	0	0	0	1
74	0	1	0	0
75	0	0	0	1
76	0	0	0	1
77	0	0	1	0
78	0	0	0	1
80	0	0	0	1
81	0	0	0	1
83	0	0	0	1
84	0	0	0	1
87	0	1	0	0
88	0	0	0	1
90	0	1	0	0
91	0	1	0	0
92	0	1	0	0
93	0	0	0	1
94	0	0	0	1
96	0	0	1	0
98	1	0	0	0
100	1	0	0	0
101	0	0	1	0
103	0	1	0	0
104	0	1	0	0
105	0	1	0	0
106	0	0	0	1
107	0	1	0	0
108	0	0	0	1
109	0	0	0	1
110	0	0	0	1
111	0	1	0	0
112	0	0	0	1
113	0	0	1	0
116	0	1	0	0
117	0	0	1	0
120	0	1	0	0
121	0	0	0	1
122	0	0	0	1
123	0	0	1	0
124	0	0	0	1
126	0	1	0	0
127	0	0	0	1
128	0	0	0	1
130	0	1	0	0
131	0	1	0	0
132	0	0	0	1
133	0	0	1	0
134	0	0	0	1
135	0	1	0	0
138	0	0	0	1
140	0	0	0	1
141	0	0	0	1
142	0	0	0	1
143	0	0	1	0
144	0	1	0	0
146	0	0	0	1
148	0	0	0	1
149	0	0	0	1
151	0	0	0	1
152	0	0	0	1
153	0	0	0	1
154	0	0	1	0
155	0	1	0	0
156	0	0	1	0
157	0	0	0	1
160	0	1	0	0
161	0	1	0	0
162	0	0	0	1
163	0	0	0	1
164	0	0	0	1
165	0	0	0	1
166	0	0	1	0
167	0	0	0	1
168	0	0	0	1
169	0	1	0	0
170	0	1	0	0
171	0	0	0	1
172	0	0	0	1
173	0	0	0	1
174	0	0	0	1
175	0	0	0	1
176	0	0	0	1
177	0	0	0	1
179	0	0	0	1
180	0	0	0	1
181	0	0	0	1
182	0	1	0	0
183	0	0	0	1
185	0	0	0	1
186	0	0	1	0
187	0	0	0	1
189	0	1	0	0
190	0	0	1	0
192	0	1	0	0
194	0	0	1	0
196	0	0	1	0
197	0	0	0	1
198	0	0	0	1
199	0	0	0	1
200	0	1	0	0
201	0	1	0	0
202	0	0	0	1
203	0	1	0	0
204	0	0	0	1
205	0	0	0	1
206	0	0	0	1
209	0	0	1	0
211	0	0	0	1
212	0	0	1	0
213	0	1	0	0
214	0	1	0	0
215	0	0	1	0
216	0	1	0	0
217	0	1	0	0
218	0	1	0	0
221	0	1	0	0
222	0	0	1	0
223	0	1	0	0
224	0	0	0	1
225	0	0	0	1
226	0	0	0	1
227	0	1	0	0
228	0	0	0	1
229	0	1	0	0
230	0	0	0	1
231	0	0	0	1
232	0	0	0	1
233	0	0	0	1
235	1	0	0	0
238	0	0	0	1
241	0	1	0	0
242	0	0	0	1
243	0	0	0	1
244	0	0	0	1
247	0	0	0	1
248	0	0	0	1
249	0	0	0	1
250	0	0	0	1
251	0	0	0	1
252	0	0	1	0
253	0	1	0	0
254	0	1	0	0
255	0	0	0	1
256	0	0	0	1
257	0	0	0	1
259	0	0	0	1
264	0	0	0	1
266	0	0	0	1
267	0	1	0	0
268	0	0	0	1
272	0	0	0	1
273	0	0	0	1
274	0	0	0	1
275	0	0	1	0
277	0	0	0	1
278	0	0	0	1
279	0	0	0	1
280	0	0	0	1
282	0	1	0	0
284	0	1	0	0
285	0	1	0	0
287	0	0	0	1
288	0	0	0	1
289	0	0	0	1
290	0	0	0	1
292	0	0	0	1
293	0	0	0	1
294	0	0	0	1
295	1	0	0	0
296	0	1	0	0
297	0	1	0	0
298	0	0	0	1
300	0	1	0	0
302	0	0	0	1
303	0	0	0	1
304	0	0	0	1
305	0	0	1	0
306	0	0	0	1
307	0	0	0	1
310	0	0	0	1
312	0	0	1	0
314	0	0	0	1
315	0	0	0	1
316	0	0	1	0
317	0	0	0	1
318	0	0	0	1
319	0	0	0	1
320	0	0	1	0
321	0	0	1	0
322	0	0	1	0
323	0	0	0	1
325	0	0	0	1
326	0	0	0	1
...	...	...	...	...
7487	0	0	0	1
7488	0	1	0	0
7489	0	0	0	1
7490	0	0	0	1
7491	0	0	0	1
7492	0	0	0	1
7493	0	0	1	0
7494	0	0	1	0
7495	0	0	1	0
7496	0	0	0	1
7497	0	0	0	1
7498	0	0	0	1
7499	0	0	0	1
7500	0	0	1	0
7501	0	0	0	1
7502	0	0	0	1
7503	0	0	0	1
7505	0	0	0	1
7506	0	0	0	1
7508	1	0	0	0
7509	1	0	0	0
7510	0	0	0	1
7511	0	0	0	1
7512	0	0	0	1
7513	0	0	0	1
7515	0	0	1	0
7516	0	0	0	1
7517	0	0	0	1
7518	0	0	1	0
7519	0	0	1	0
7520	0	0	0	1
7521	0	0	1	0
7523	0	0	1	0
7525	0	0	0	1
7526	0	0	0	1
7527	0	0	0	1
7529	0	0	0	1
7530	0	1	0	0
7531	0	0	0	1
7532	0	0	1	0
7533	0	0	1	0
7534	0	0	1	0
7535	0	0	1	0
7536	0	0	1	0
7537	0	0	0	1
7538	0	1	0	0
7539	0	0	1	0
7540	0	0	1	0
7541	0	0	0	1
7542	0	0	1	0
7543	0	0	0	1
7544	0	0	0	1
7545	0	0	0	1
7546	0	0	0	1
7547	0	0	0	1
7548	0	0	0	1
7549	0	0	1	0
7552	0	0	0	1
7553	0	0	0	1
7554	0	0	0	1
7555	0	0	0	1
7556	0	1	0	0
7557	0	0	1	0
7558	0	0	0	1
7560	0	0	0	1
7561	0	0	0	1
7562	0	0	0	1
7564	0	0	1	0
7565	0	0	0	1
7566	0	0	0	1
7567	0	0	0	1
7568	0	0	0	1
7569	1	0	0	0
7571	0	0	0	1
7572	0	0	0	1
7573	0	0	1	0
7575	0	0	1	0
7576	0	0	1	0
7579	0	0	0	1
7580	0	0	0	1
7581	0	0	0	1
7582	0	0	0	1
7583	0	0	0	1
7584	0	1	0	0
7585	0	0	0	1
7586	0	1	0	0
7587	0	0	1	0
7588	0	1	0	0
7589	0	0	0	1
7590	0	0	0	1
7591	0	0	0	1
7592	0	0	0	1
7593	0	0	0	1
7594	0	0	0	1
7595	0	0	0	1
7596	0	0	1	0
7597	0	0	0	1
7598	0	0	1	0
7600	0	0	1	0
7604	0	0	0	1
7605	0	0	0	1
7606	0	0	0	1
7609	0	0	1	0
7611	0	0	0	1
7612	1	0	0	0
7613	0	0	1	0
7614	0	1	0	0
7616	0	0	1	0
7617	0	0	0	1
7618	0	0	0	1
7624	0	0	0	1
7626	0	0	0	1
7627	0	0	0	1
7628	0	0	0	1
7634	0	1	0	0
7635	0	0	0	1
7636	0	0	0	1
7637	0	0	0	1
7638	0	0	0	1
7639	0	0	1	0
7640	0	0	0	1
7641	0	0	0	1
7642	0	0	0	1
7643	0	0	1	0
7647	0	0	0	1
7648	0	1	0	0
7650	0	0	0	1
7651	0	1	0	0
7652	0	0	0	1
7653	0	0	0	1
7654	0	0	1	0
7655	0	0	0	1
7656	0	0	0	1
7657	0	0	1	0
7658	0	0	0	1
7661	0	0	1	0
7662	0	0	0	1
7663	0	1	0	0
7664	0	0	0	1
7665	0	0	0	1
7666	0	0	0	1
7667	0	0	0	1
7668	0	1	0	0
7671	0	0	0	1
7672	0	0	1	0
7673	0	0	1	0
7674	0	1	0	0
7675	0	0	0	1
7676	0	0	0	1
7677	0	0	1	0
7679	0	0	1	0
7680	0	0	0	1
7681	0	0	0	1
7683	0	0	0	1
7686	0	0	0	1
7688	0	0	0	1
7689	0	0	0	1
7690	0	0	0	1
7691	0	0	0	1
7694	0	0	0	1
7695	0	0	0	1
7698	0	0	0	1
7699	0	0	0	1
7700	0	0	0	1
7702	0	0	0	1
7703	0	0	0	1
7704	0	0	0	1
7705	0	0	0	1
7707	0	0	0	1
7708	0	0	0	1
7709	0	0	0	1
7710	0	0	0	1
7711	0	0	0	1
7712	0	0	0	1
7713	0	0	0	1
7716	0	0	0	1
7717	0	0	0	1
7718	0	0	0	1
7720	0	0	0	1
7721	0	0	0	1
7722	0	0	1	0
7723	0	0	1	0
7724	0	0	1	0
7725	0	0	0	1
7727	0	0	1	0
7728	0	0	0	1
7729	0	0	0	1
7730	0	0	0	1
7731	0	0	0	1
7732	0	0	0	1
7733	0	0	0	1
7734	0	0	0	1
7735	0	0	1	0
7737	0	0	1	0
7738	0	0	0	1
7739	0	0	0	1
7740	0	1	0	0
7741	0	0	0	1
7742	0	0	0	1
7743	0	0	0	1
7744	0	0	0	1
7745	0	0	0	1
7746	0	1	0	0
7747	0	0	0	1
7748	0	0	0	1
7750	0	0	0	1
7752	0	0	0	1
7753	0	0	0	1
7755	0	1	0	0
7756	0	0	0	1
7758	0	1	0	0
7760	0	0	0	1
7762	0	0	0	1
7763	0	0	0	1
7764	0	0	0	1
7768	0	1	0	0
7770	0	0	0	1
7771	0	0	0	1
7772	0	0	0	1
7774	0	0	0	1
7775	0	0	0	1
7779	0	0	1	0
7780	0	0	1	0
7782	0	0	1	0
7784	0	0	0	1
7785	0	0	0	1
7788	0	0	0	1
7790	0	0	0	1
7794	0	0	0	1
7795	0	0	0	1
7796	0	0	0	1
7798	0	0	0	1
7799	0	1	0	0
7801	0	0	0	1
7802	0	0	0	1
7803	0	0	0	1
7804	0	0	0	1
7805	0	0	0	1
7807	0	0	0	1
7808	0	0	0	1
7810	0	0	0	1
7811	0	0	0	1
7814	0	0	0	1
7816	0	0	0	1
7817	0	0	0	1
7821	0	0	0	1
7822	0	0	0	1
7825	0	0	0	1
7828	0	0	0	1
7832	0	0	1	0

	calculated_host_listings_count
neighbourhood_cleansed
Mission	569
South of Market	484
Western Addition	467
Castro/Upper Market	322
Downtown/Civic Center	313
Haight Ashbury	304
Bernal Heights	300
Noe Valley	256
Nob Hill	253
Outer Sunset	246
Other	237
Potrero Hill	174
Bayview	161
Inner Richmond	153
Outer Mission	144
Excelsior	144
North Beach	133
Marina	125
Inner Sunset	124
Outer Richmond	124
Parkside	121
Russian Hill	114
Ocean View	108
West of Twin Peaks	103
Financial District	97
Pacific Heights	95
Chinatown	88
Visitacion Valley	70

	calculated_host_listings_count
property_type
Apartment	2343
House	1779
Condominium	607
Guest suite	430
Boutique hotel	131
Townhouse	115
Hostel	74
Serviced apartment	71
Hotel	68
Loft	58
Bed and breakfast	33
Guesthouse	30
Aparthotel	23
Other	22
Bungalow	12
Resort	9
Cottage	8
Villa	8
Cabin	2
Tiny house	2
Earth house	1
Dome house	1
Camper/RV	1
Boat	1

	calculated_host_listings_count
cancellation_policy
strict_14_with_grace_period	2475
moderate	2073
flexible	1175
strict	73
super_strict_30	23
super_strict_60	10

	calculated_host_listings_count
cancellation_policy
strict_with_grace_period	2475
moderate	2073
flexible	1175
strict	73
super_strict	33

	price
bedrooms
4.0	343.709677
5.0	335.333333
3.0	298.287640
2.0	240.757682
1.0	135.579710

	price
bathrooms
0.0	74.333333
0.5	84.894737
1.0	159.199314
1.5	185.179245
2.0	241.991463
2.5	231.355072
3.0	187.702128
3.5	220.516129
4.0	102.375000
4.5	342.750000
5.0	88.848485

	Model Type	Parameters	Test Score	Training_score
Model Name
Linear Regression	Regression	-	5.746322e-01	6.277386e-01
Polynomial Regression	Regression	{'Degree': 2}	-1.365666e+18	-9.918478e+18
KNN Regressor	Regression	{'n_neighbors': 10}	5.103967e-01	6.393940e-01
Ridge Regression	Regression	{'alpha': 100}	5.750267e-01	6.276089e-01
Lasso Regression	Regression	{'alpha': 0.1}	5.747416e-01	6.277062e-01
Linear SVM	Regression	{'C': 1, 'gamma': 0.001}	5.527286e-01	6.081529e-01
SVM Radial	Regression	{'C': 100, 'gamma': 0.01}	6.129452e-01	7.965109e-01
Polynomial SVM	Regression	{'C': 100}	5.408060e-01	7.464654e-01
XGBRegressor	Regression	NaN	6.489768e-01	7.172015e-01